{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                计算机视觉的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概述\n",
    "\n",
    "计算机视觉是当前深度学习研究最广泛、落地最成熟的技术领域，在手机拍照、智能安防、自动驾驶等场景有广泛应用。从2012年AlexNet在ImageNet比赛夺冠以来，深度学习深刻推动了计算机视觉领域的发展，当前最先进的计算机视觉算法几乎都是深度学习相关的。深度神经网络可以逐层提取图像特征，并保持局部不变性，被广泛应用于分类、检测、分割、跟踪、检索、识别、提升、重建等视觉任务中。\n",
    "本次体验结合图像分类任务，介绍MindSpore如何应用于计算机视觉场景，如何训练模型，得出一个性能较优的模型。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图像分类\n",
    "\n",
    "图像分类是最基础的计算机视觉应用，属于有监督学习类别。给定一张数字图像，判断图像所属的类别，如猫、狗、飞机、汽车等等。用函数来表示这个过程如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def classify(image):\n",
    "   label = model(image)\n",
    "   return label\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义的分类函数，以图片数据`image`为输入，通过`model`方法对`image`进行分类，最后返回分类结果。选择合适的`model`是关键。这里的`model`一般指的是深度卷积神经网络，如AlexNet、VGG、GoogLeNet、ResNet等等。  \n",
    "下面按照MindSpore的训练数据模型的正常步骤进行，当使用到MindSpore或者图像分类操作时，会增加相应的说明，本次体验的整体流程如下：\n",
    "\n",
    "1. 数据集的准备，这里使用的是CIFAR-10数据集。\n",
    "\n",
    "2. 构建一个卷积神经网络，这里使用ResNet-50网络。\n",
    "\n",
    "3. 定义损失函数和优化器。\n",
    "\n",
    "4. 调用Model高阶API进行训练和保存模型文件。\n",
    "\n",
    "5. 进行模型精度验证。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 本文档适用于GPU和Ascend环境。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练数据集下载\n",
    "\n",
    "### 数据集准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-12-04 11:34:20--  https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/datasets/cifar10.zip\n",
      "Resolving proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)... 192.168.0.172\n",
      "Connecting to proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)|192.168.0.172|:8083... connected.\n",
      "Proxy request sent, awaiting response... 200 OK\n",
      "Length: 166235630 (159M) [application/zip]\n",
      "Saving to: ‘cifar10.zip’\n",
      "\n",
      "cifar10.zip         100%[===================>] 158.53M  56.2MB/s    in 2.8s    \n",
      "\n",
      "2020-12-04 11:34:23 (56.2 MB/s) - ‘cifar10.zip’ saved [166235630/166235630]\n",
      "\n",
      "Archive:  cifar10.zip\n",
      "   creating: ./datasets/cifar10/\n",
      "   creating: ./datasets/cifar10/test/\n",
      "  inflating: ./datasets/cifar10/test/test_batch.bin  \n",
      "   creating: ./datasets/cifar10/train/\n",
      "  inflating: ./datasets/cifar10/train/batches.meta.txt  \n",
      "  inflating: ./datasets/cifar10/train/data_batch_1.bin  \n",
      "  inflating: ./datasets/cifar10/train/data_batch_2.bin  \n",
      "  inflating: ./datasets/cifar10/train/data_batch_3.bin  \n",
      "  inflating: ./datasets/cifar10/train/data_batch_4.bin  \n",
      "  inflating: ./datasets/cifar10/train/data_batch_5.bin  \n",
      "./datasets/cifar10\n",
      "├── test\n",
      "│   └── test_batch.bin\n",
      "└── train\n",
      "    ├── batches.meta.txt\n",
      "    ├── data_batch_1.bin\n",
      "    ├── data_batch_2.bin\n",
      "    ├── data_batch_3.bin\n",
      "    ├── data_batch_4.bin\n",
      "    └── data_batch_5.bin\n",
      "\n",
      "2 directories, 7 files\n"
     ]
    }
   ],
   "source": [
    "!wget -N https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/datasets/cifar10.zip\n",
    "!unzip -o cifar10.zip -d ./datasets\n",
    "!tree ./datasets/cifar10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理\n",
    "\n",
    "数据集处理对于训练非常重要，好的数据集可以有效提高训练精度和效率。在加载数据集前，我们通常会对数据集进行一些处理。这里我们用到了数据增强，数据混洗和批处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据增强主要是对数据进行归一化和丰富数据样本数量。常见的数据增强方式包括裁剪、翻转、色彩变化等等。MindSpore通过调用`map`方法在图片上执行增强操作。数据混洗和批处理主要是通过数据混洗`shuffle`随机打乱数据的顺序，并按`batch`读取数据，进行模型训练。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建`create_dataset`函数，来创建数据集。通过设置` resize_height`、`resize_width`、`rescale`、`shift`参数，定义`map`以及在图片上运用`map`实现数据增强。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset size is: 1562\n",
      "The batch tensor is: (32, 3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mindspore.nn as nn\n",
    "from mindspore import dtype as mstype\n",
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.vision.c_transforms as C\n",
    "import mindspore.dataset.transforms.c_transforms as C2\n",
    "from mindspore import context\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")\n",
    "\n",
    "def create_dataset(data_home, repeat_num=1, batch_size=32, do_train=True, device_target=\"GPU\"):\n",
    "    \"\"\"\n",
    "    create data for next use such as training or inferring\n",
    "    \"\"\"\n",
    "\n",
    "    cifar_ds = ds.Cifar10Dataset(data_home,num_parallel_workers=8, shuffle=True)\n",
    "\n",
    "    c_trans = []\n",
    "    if do_train:\n",
    "        c_trans += [\n",
    "            C.RandomCrop((32, 32), (4, 4, 4, 4)),\n",
    "            C.RandomHorizontalFlip(prob=0.5)\n",
    "        ]\n",
    "\n",
    "    c_trans += [\n",
    "        C.Resize((224, 224)),\n",
    "        C.Rescale(1.0 / 255.0, 0.0),\n",
    "        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n",
    "        C.HWC2CHW()\n",
    "    ]\n",
    "\n",
    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
    "\n",
    "    cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n",
    "    cifar_ds = cifar_ds.map(operations=c_trans, input_columns=\"image\", num_parallel_workers=8)\n",
    "\n",
    "    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\n",
    "    cifar_ds = cifar_ds.repeat(repeat_num)\n",
    "\n",
    "    return cifar_ds\n",
    "\n",
    "\n",
    "ds_train_path = \"./datasets/cifar10/train/\"\n",
    "dataset_show = create_dataset(ds_train_path)\n",
    "with open(ds_train_path+\"batches.meta.txt\",\"r\",encoding=\"utf-8\") as f:\n",
    "    all_name = [name.replace(\"\\n\",\"\") for name in f.readlines()]\n",
    "\n",
    "iterator_show= dataset_show.create_dict_iterator()\n",
    "dict_data = next(iterator_show)\n",
    "images = dict_data[\"image\"].asnumpy()\n",
    "labels = dict_data[\"label\"].asnumpy()\n",
    "count = 1\n",
    "%matplotlib inline\n",
    "for i in images:\n",
    "    plt.subplot(4, 8, count)\n",
    "    # Images[0].shape is (3,224,224).We need transpose as (224,224,3) for using in plt.show().\n",
    "    picture_show = np.transpose(i,(1,2,0))\n",
    "    picture_show = picture_show/np.amax(picture_show)\n",
    "    picture_show = np.clip(picture_show, 0, 1)\n",
    "    plt.title(all_name[labels[count-1]])\n",
    "    picture_show = np.array(picture_show,np.float32)\n",
    "    plt.imshow(picture_show)\n",
    "    count += 1\n",
    "    plt.axis(\"off\")\n",
    "\n",
    "print(\"The dataset size is:\", dataset_show.get_dataset_size())\n",
    "print(\"The batch tensor is:\",images.shape)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集生成后，选取一个`batch`的图像进行可视化查看，经过数据增强后，原数据集变成了每个batch张量为，共计1572个batch的新数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义卷积神经网络\n",
    "\n",
    "卷积神经网络已经是图像分类任务的标准算法了。卷积神经网络采用分层的结构对图片进行特征提取，由一系列的网络层堆叠而成，比如卷积层、池化层、激活层等等。\n",
    "ResNet-50通常是较好的选择。首先，它足够深，常见的有34层，50层，101层。通常层次越深，表征能力越强，分类准确率越高。其次，可学习，采用了残差结构，通过shortcut连接把低层直接跟高层相连，解决了反向传播过程中因为网络太深造成的梯度消失问题。此外，ResNet-50网络的性能很好，既表现为识别的准确率，也包括它本身模型的大小和参数量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下载构建好的resnet50网络源码文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-12-04 11:34:29--  https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/source-codes/resnet.py\n",
      "Resolving proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)... 192.168.0.172\n",
      "Connecting to proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)|192.168.0.172|:8083... connected.\n",
      "Proxy request sent, awaiting response... 200 OK\n",
      "Length: 9533 (9.3K) [binary/octet-stream]\n",
      "Saving to: ‘resnet.py’\n",
      "\n",
      "resnet.py           100%[===================>]   9.31K  --.-KB/s    in 0s      \n",
      "\n",
      "2020-12-04 11:34:29 (160 MB/s) - ‘resnet.py’ saved [9533/9533]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -N https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/source-codes/resnet.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下载下来的`resnet.py`在当前目录，可以使用`import`方法将resnet50网络导出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from resnet import resnet50\n",
    "\n",
    "net = resnet50(batch_size=32, num_classes=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义损失函数和优化器\n",
    "\n",
    "接下来需要定义损失函数（Loss）和优化器（Optimizer）。损失函数是深度学习的训练目标，也叫目标函数，可以理解为神经网络的输出（Logits）和标签(Labels)之间的距离，是一个标量数据。\n",
    "常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（CrossEntropy）。\n",
    "优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如SGD、ADAM、Momemtum等等。本例采用Momentum优化器，通常需要设定两个参数，动量（moment）和权重衰减项（weight decay）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过调用MindSpore中的API：`Momentum`和`SoftmaxCrossEntropyWithLogits`，设置损失函数和优化器的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.nn as nn\n",
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits\n",
    "\n",
    "ls = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
    "opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调用Model高阶API进行训练和保存模型文件\n",
    "\n",
    "完成数据预处理、网络定义、损失函数和优化器定义之后，就可以进行模型训练了。模型训练包含两层迭代，数据集的多轮迭代（epoch）和一轮数据集内按分组（batch）大小进行的单步迭代。其中，单步迭代指的是按分组从数据集中抽取数据，输入到网络中计算得到损失函数，然后通过优化器计算和更新训练参数的梯度。\n",
    "\n",
    "为了简化训练过程，MindSpore封装了Model高阶接口。用户输入网络、损失函数和优化器完成Model的初始化，然后调用`train`接口进行训练，`train`接口参数包括迭代次数`epoch`和数据集`dataset`。\n",
    "\n",
    "模型保存是对训练参数进行持久化的过程。`Model`类中通过回调函数的方式进行模型保存，如下面代码所示。用户通过`CheckpointConfig`设置回调函数的参数，其中，`save_checkpoint_steps`指每经过固定的单步迭代次数保存一次模型，`keep_checkpoint_max`指最多保存的模型个数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本次体验选择`epoch_size`为10，一共迭代了10次，得到如下的运行结果。体验者可以自行设置不同的`epoch_size`，生成不同的模型，在下面的验证部分查看模型精确度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 step: 142, loss is 2.041043\n",
      "epoch: 1 step: 284, loss is 1.7856436\n",
      "epoch: 1 step: 426, loss is 1.5305918\n",
      "epoch: 1 step: 568, loss is 1.7724597\n",
      "epoch: 1 step: 710, loss is 1.3827077\n",
      "epoch: 1 step: 852, loss is 1.2597129\n",
      "epoch: 1 step: 994, loss is 1.7239776\n",
      "epoch: 1 step: 1136, loss is 1.5275167\n",
      "epoch: 1 step: 1278, loss is 1.5193802\n",
      "epoch: 1 step: 1420, loss is 1.2839599\n",
      "epoch: 1 step: 1562, loss is 1.2304927\n",
      "epoch: 2 step: 142, loss is 1.3567938\n",
      "epoch: 2 step: 284, loss is 0.9500004\n",
      "epoch: 2 step: 426, loss is 1.022787\n",
      "epoch: 2 step: 568, loss is 1.1582599\n",
      "epoch: 2 step: 710, loss is 0.96332455\n",
      "epoch: 2 step: 852, loss is 1.0251642\n",
      "... ...\n",
      "epoch: 10 step: 426, loss is 0.15948972\n",
      "epoch: 10 step: 568, loss is 0.30928737\n",
      "epoch: 10 step: 710, loss is 0.26390004\n",
      "epoch: 10 step: 852, loss is 0.33643064\n",
      "epoch: 10 step: 994, loss is 0.38283178\n",
      "epoch: 10 step: 1136, loss is 0.26065817\n",
      "epoch: 10 step: 1278, loss is 0.48519582\n",
      "epoch: 10 step: 1420, loss is 0.43286812\n",
      "epoch: 10 step: 1562, loss is 0.42926747\n"
     ]
    }
   ],
   "source": [
    "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor\n",
    "from mindspore import load_checkpoint, load_param_into_net\n",
    "import os\n",
    "from mindspore import Model\n",
    "\n",
    "\n",
    "model = Model(net, loss_fn=ls, optimizer=opt, metrics={'acc'})\n",
    "# As for train, users could use model.train\n",
    "\n",
    "epoch_size = 10\n",
    "ds_train_path = \"./datasets/cifar10/train/\"\n",
    "model_path = \"./models/ckpt/mindspore_vision_application/\"\n",
    "os.system('rm -f {0}*.ckpt {0}*.meta {0}*.pb'.format(model_path))\n",
    "\n",
    "dataset = create_dataset(ds_train_path )\n",
    "batch_num = dataset.get_dataset_size()\n",
    "config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=35)\n",
    "ckpoint_cb = ModelCheckpoint(prefix=\"train_resnet_cifar10\", directory=model_path, config=config_ck)\n",
    "loss_cb = LossMonitor(142)\n",
    "model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查询训练过程中，保存好的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./models/ckpt/mindspore_vision_application/\n",
      "├── train_resnet_cifar10-10_1562.ckpt\n",
      "├── train_resnet_cifar10-1_1562.ckpt\n",
      "├── train_resnet_cifar10-2_1562.ckpt\n",
      "├── train_resnet_cifar10-3_1562.ckpt\n",
      "├── train_resnet_cifar10-4_1562.ckpt\n",
      "├── train_resnet_cifar10-5_1562.ckpt\n",
      "├── train_resnet_cifar10-6_1562.ckpt\n",
      "├── train_resnet_cifar10-7_1562.ckpt\n",
      "├── train_resnet_cifar10-8_1562.ckpt\n",
      "├── train_resnet_cifar10-9_1562.ckpt\n",
      "└── train_resnet_cifar10-graph.meta\n",
      "\n",
      "0 directories, 11 files\n"
     ]
    }
   ],
   "source": [
    "!tree ./models/ckpt/mindspore_vision_application/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每1562个step保存一次模型权重参数`.ckpt`文件，一共保存了10个，另外`.meta`文件保存模型的计算图信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行模型精度验证\n",
    "\n",
    "调用`model.eval`得到最终精度数据约为0.80远高于0.5，准确度较高，验证得出模型是性能较优的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result:  {'acc': 0.8001802884615384}\n"
     ]
    }
   ],
   "source": [
    "# As for evaluation, users could use model.eval\n",
    "ds_eval_path = \"./datasets/cifar10/test/\"\n",
    "eval_dataset = create_dataset(ds_eval_path, do_train=False)\n",
    "res = model.eval(eval_dataset)\n",
    "print(\"result: \", res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "本次体验，带领体验者了解了MindSpore的卷积神经网络ResNet-50，通过构建ResNet-50对CIFAR-10进行分类。可以看出MindSpore的ResNet-50的构建非常容易，损失函数和优化器都有封装好的API，对于初学者和研发人员都非常的友善。"
   ]
  }
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